Method and system for determining traffic-related characteristics

ABSTRACT

A method for traffic characterization associated with a vehicle including collecting a movement dataset sampled at least at one of a location sensor and a motion sensor associated with the vehicle, during a driving session associated with movement of the vehicle; extracting a set of features from the movement dataset associated with movement of the vehicle during the driving session; and determining one or more traffic-related characteristics associated with the vehicle based on the set of features.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/022,184 filed 28 Jun. 2018, which claims the benefit of U.S.Provisional Application No. 62/526,113, filed 28 Jun. 2017, which areincorporated herein in their entirety by this reference. Thisapplication is related to U.S. application Ser. No. 15/727,972, filed 9Oct. 2017, which is incorporated herein in its entirety by thisreference.

TECHNICAL FIELD

This invention relates generally to the vehicle monitoring field, andmore specifically to a new and useful method and system for determiningtraffic-related characteristics associated with one or more vehicles.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of an embodiment of a method;

FIG. 2 is a graphical representation of a variation of an embodiment ofthe method;

FIG. 3 is a graphical representation of a variation of an embodiment ofthe method;

FIG. 4 depicts a flowchart representation of an example implementationof an embodiment of the method; and

FIG. 5 depicts a flowchart representation of an example implementationof an embodiment of the method.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. OVERVIEW

As shown in FIGS. 1-3, embodiments of a method 100 for trafficcharacterization associated with a vehicle can include: collecting amovement dataset sampled at least at one of a location sensor and amotion sensor associated with the vehicle, during a driving sessionassociated with movement of the vehicle (e.g., a time period duringwhich the vehicle moves) S110; extracting a set of features from themovement dataset associated with movement of the vehicle during thedriving session S130; and determining one or more traffic-relatedcharacteristics associated with the vehicle based on the set of featuresS140. Additionally or alternatively, the method 100 can include:collecting a supplementary dataset configured to supplement the movementdataset S120; initiating a traffic-related action based on the one ormore traffic-related characteristics S150; and/or any other suitableprocesses.

Embodiments of the method 100 and/or system 200 function to characterizeone or more vehicles' interactions with one or more aspects related totraffic (e.g., the vehicle's compliance with traffic laws;identification of traffic indicators associated with the vehicle'sroute; etc.) to aid users (e.g., drivers; users residing in and/or onthe vehicle; users associated with the driving session; usersindependent of the driving session; etc.) and/or other entities (e.g.,third parties such as insurance entities; etc.) in relation toevaluating, improving, and/or otherwise processing traffic-relatedcharacteristics. In a specific example, the method 100 and/or system 200can determine traffic-related characteristics for a vehicle based onmovement features of the vehicle and other vehicles (e.g., throughcomparing a vehicle's movement features relative to movement features ofgeographically proximal vehicles on the same vehicular path during thecurrent time period or a historic time period, such as to determinespeed limit compliance, etc.), in relation to traffic regulationssurrounding posted speed limitations and/or other speed limitationsestablished by other traffic or environmental conditions. Additionallyor alternatively, the method 100 and/or system 200 can function toleverage the traffic-related characteristics in determining and/orpromoting traffic-related actions. For example, the method 100 and/orsystem 200 can enable real-time traffic characterization based on atleast one of movement data and supplemental data to automaticallygenerate traffic-related educational guidance (e.g., optimized routeinformation).

The method 100 is preferably implemented on one or more mobile devicesremovably coupled to one or more vehicles (e.g., residing in thevehicle; mounted to the vehicle and/or component coupled to the vehicle;physically coupled to the vehicle; communicatively coupled to thevehicle; etc.). Additionally or alternatively, the method 100 can beimplemented by any number of suitable devices (e.g., including thevehicle itself; remote computing systems; etc.). One or more instancesand/or portions of the method 100 and/or processes described herein canbe performed asynchronously (e.g., sequentially), concurrently (e.g., inparallel; concurrently on different threads for parallel computing toimprove system processing ability for determining and/or otherwiseprocessing traffic-related characteristics; etc.), in temporal relationto a trigger event, and/or in any other suitable order at any suitabletime and frequency by and/or using one or more instances of the system200 (e.g., a system 200 including processing systems such as remotecomputing systems; sensors; vehicles; mobile devices; etc.), elements,and/or entities described herein. However, the method 100 and/or system200 can be configured in any suitable manner.

2. BENEFITS

In specific examples, the method 100 and/or system 200 can conferseveral benefits over conventional methodologies used for determiningtraffic related characteristics. In specific examples and/or variations,the method 100 and/or system 200 can perform one or more of thefollowing:

First, the technology can leverage non-generic location data (e.g.,location datasets, GPS data, etc.) and/or motion data (e.g., motiondatasets, accelerometer data, gyroscope data, etc.) to conveniently andunobtrusively determine a traffic-related characteristic. In examples,the location data and/or motion data can be passively collected at auser's mobile computing device (e.g., a smartphone), such that thetechnology can perform traffic characteristic determination withoutrequiring a user to purchase additional hardware (e.g., a specializedonboard device for monitoring traffic-related characteristics, apurpose-built such device, etc.).

Second, the technology can determine traffic-related characteristicswith high accuracy. In examples, the technology can accurately infertraffic laws (e.g., traffic regulations, traffic rules, de facto trafficrules based on aggregated traffic data, etc.) based on movement datacollected from the field, thereby minimizing the use of third partydatasets that compile traffic regulations for various geographiclocations, which can be outdated, inaccurate, or otherwise unsuitable.Further, the technology can provide accurate traffic-relatedcharacteristic determination while allowing a user to have flexibilityin placement of their mobile computing device in relation to the vehicle(e.g., motor vehicles, bicycles, watercraft, aircraft, spacecraft,railed vehicles, etc.). Traffic-related characteristics can bedetermined when the mobile computing device collecting movement data isin a user's pocket, in a user's hand, mounted within the car, in a cupholder, and/or in other suitable locations within the vehicle.

Third, the technology can automatically initiate traffic-related actionsin response to determination of traffic-related characteristics.Traffic-related actions can include any one or more of controlling theuser device, sending a notification to the user or a related entity,generating user-specific content, and/or other suitable user-relatedactions. Further, in examples, the type of initiated traffic-relatedaction can be tailored to the particular traffic-related characteristicbased on movement data (e.g., motion data, location data, etc.) and/orsupplemental data. In examples, the technology can include a softwaredevelopment kit for third-parties to integrate the traffic-relatedcharacteristic determination features, thereby enabling third-parties toleverage traffic-related characteristics and related data for purposesof driving education, ride sharing, valet services, navigation, roadsideassistance, insurance processing, emergency services, and/or othersuitable services.

Fourth, the technology can determine traffic-related characteristics inreal-time (e.g., instantaneously, substantially real-time, nearreal-time, etc.). Prompt determination of traffic-relatedcharacteristics (e.g., compliance with traffic laws or regulations,deviation from ambient traffic characteristics, etc.) can enable timelyprovision (e.g. during the in-trip time period) of appropriate messages(e.g., driver specific messages, passenger specific messages, movingviolation specific messages, compliance specific messages, noncompliancespecific messages, etc.) to the user.

Fifth, the technology can improve the technical fields of at leastvehicle telematics, computational modeling of traffic-relatedcharacteristics, and traffic-related characteristic determination withmobile computing device data. The technology can continuously collectand utilize non-generic sensor data (e.g., location sensor data, motionsensor data, GPS data, audio/visual data, ambient light data, etc.) toprovide real-time determination of traffic-related characteristics.Further, the technology can take advantage of the non-generic sensordata and/or supplemental data (e.g., vehicle sensor data, weather data,traffic data, environmental data, biosignal sensor data, etc.) to betterimprove the understanding of correlations between such data andtraffic-related characteristic, leading to an increased understanding ofvariables affecting user behavior while driving and/or riding in avehicle and/or traffic behavior at the scale of a population of usersdriving vehicles.

Sixth, the technology can provide technical solutions necessarily rootedin computer technology (e.g., utilizing computational models todetermine user characteristics from non-generic location and/or motiondatasets collected at mobile computing devices, etc.) to overcome issuesspecifically arising with computer technology (e.g., issues surroundinghow to leverage movement data collected by a mobile computing device todetermine traffic-related characteristics, and/or to automaticallyinitiate traffic-related actions for responding to traffic-relatedcharacterization, etc.).

Seventh, the technology can leverage specialized computing devices(e.g., computing devices with GPS location capabilities, computingdevices with motion sensor functionality, etc.) to collect specializeddatasets for characterizing traffic behaviors executed by the vehicle(e.g., under the influence of the driver's control, when controlled byan autonomous control system, etc.).

Eighth, the technology can enable prompt collision detection (e.g.,substantially as described in U.S. application Ser. No. 15/727,972,filed 9 Oct. 2017, which is incorporated herein in its entirety by thisreference), and correlation of the collision with whether the user wasin compliance with traffic regulations at the time of the collision(e.g., to determine a fault status of the user, to determine a not-faultstatus of the user, etc.).

The technology can, however, provide any other suitable benefit(s) inthe context of using non-generalized computer systems for determiningone or more traffic-related characteristics and initiatingtraffic-related actions.

3.1 METHOD—COLLECTING A MOVEMENT DATASET

Block S110 recites: collecting a movement dataset sampled at least atone of a location sensor and a motion sensor associated with thevehicle, during a time period of movement of the vehicle. Block S110functions to collect movement-related data for use in evaluating one ormore traffic-related characteristics associated with vehicles (e.g.,motor vehicles, bicycles, watercraft, aircraft, spacecraft, railedvehicles, etc.), users (e.g., a vehicle driver, vehicle passenger,pedestrian, etc.) and/or other suitable entities.

Movement datasets preferably describe at least one of position,velocity, and/or acceleration (PVA) of one or more vehicles, userdevices (e.g., user smartphones), users, and/or any other suitableentities, but can additionally or alternatively describe any suitablemovement-related characteristic. For example, movement dataset collectedat a first entity (e.g., sampled at a sensor of a user device) candescribe movement of the first entity (e.g., movement dataset describingPVA of the vehicle in which the user device resides) and/or one or moreother entities (e.g., movement dataset sampled at a sensor of a firstuser device residing in a first vehicle, where the movement datasetdescribes PVA of a second vehicle geographically proximal the firstvehicle, etc.). Movement datasets are preferably sampled at one or moremovement sensors indicative of one or more of motion and/or location,which can include one or more of: motion sensors (e.g., multi-axisand/or single-axis accelerometers, gyroscopes, etc.), location sensors(e.g., GPS data collection components, magnetometer, compass, altimeter,etc.), sensors associated with vehicle diagnostic systems (e.g., OBD-I,OBD-1.5, OBD-II, CAN, etc.), optical sensors, audio sensors,electromagnetic (EM)-related sensors (e.g., radar, lidar, sonar,ultrasound, infrared radiation, magnetic positioning, etc.),environmental sensors (e.g., temperature sensors, etc.), biometricsensors, and/or any other suitable sensors.

Movement datasets preferably include both location datasets (e.g., datadescribing the location of the sensor collecting the movement dataset)and motion datasets (e.g., data describing the motion of the sensorcollecting the movement datasets). However, a movement dataset can, insome variations, include only a location dataset or only a motiondataset. In still further variations, the method can include collectinga movement dataset that includes only a motion dataset at a first timepoint, and collecting a movement dataset that includes only a locationdataset at a second time point, wherein the first time point can precedethe second time point, the second time point can precede the first timepoint, and/or the first and second time points can be contemporaneous(e.g., simultaneous, substantially simultaneous, proximal in time,etc.).

Block S110 preferably includes collecting movement datasets associatedwith different traffic-related situations including vehicle interaction(e.g., historic, current, and/or predicted interaction) with one ormore: traffic laws (e.g., PVA data for a vehicle in the context ofapplicable traffic laws, traffic regulations, traffic rules, etc.),traffic indicators (e.g., PVA data in relation to compliance withtraffic indicators, etc.) and/or other suitable traffic parameters.Traffic laws (e.g., regulations) can include any one or more of: drivingrules, vehicle code such as Department of Motor Vehicles (e.g., DMV,bureau of motor vehicles/BMV, motor vehicle department/MVD, etc.) code,moving violations (e.g., speed limits, reckless driving, driving underthe influence, illegal turns, illegal lane changes, incompliance ornoncompliance with traffic indicators, etc.), non-moving violations(e.g., parking violations, license violations, paperwork violations,equipment violations, etc.), and/or any other suitable laws orregulations associated with vehicles. Traffic indicators can include anyone or more of: traffic signs (e.g., stop signs, warning signs,regulatory signs, guide signs, yield signs, construction signs,recreational signs, service signs, marker signs, etc.), traffic lights(e.g., street lights, signal lights, vehicle lights, supplemental devicelights, building lights, etc.), traffic markings (e.g., road surfacemarkings such as yellow and/or white lines; bike lane markings; yieldlines; lane markings; turnout markings; passing lanes; carpool lanes;turn indicators; etc.), and/or any other suitable indicators oftraffic-related aspects. In a specific example, the method 100 caninclude collecting a movement dataset; identifying a locationcorresponding to the movement dataset; mapping (e.g., at atraffic-related characteristic database, at a remote database, etc.) thelocation to a set of traffic parameters applicable to the location(e.g., to a traffic regulation applicable to the location; and storingthe movement dataset in association with the set of traffic parameters.In the aforementioned specific example and related examples, mapping thelocation to the set of traffic parameters can tagging, labeling, and/orotherwise associating the movement dataset to the location. In anotherexample, the method 100 can include determining an indicator typeassociated with a visual traffic indicator, based on movement features(e.g. a vehicle motion characteristic) wherein the vehicle motioncharacteristic characterizes a vehicle interaction with the visualtraffic indicator; associating the indicator type with the vehiclelocation; and storing the indicator type in association with the vehiclelocation at a traffic map stored at a remote computing system. In thisexample, the type of a visual traffic indicator (e.g., a stop sign, atraffic light, etc.) can be inferred based on motion characteristics(e.g., wherein a vehicle stopping for a predetermined time periodcorresponds to a stop sign at a four way intersection, wherein a vehicleexhibits a pattern of accelerometer signals corresponding to approachinga red light that changes to a green light, etc.) without the use ofoptical data. However, processing movement datasets in relation totraffic-related situations can be performed in any suitable manner.

Block S110 preferably includes collecting one or more location datasets,which can include any one or more of: GPS data (e.g., positioncoordinates, associated time stamps, etc.), geolocation data (e.g.,indicating county, city, country, and/or other suitable geographicregion corresponding to a specific set of traffic parameters, etc.),geofence data (e.g., whether the location dataset corresponds to alocation inside a geofence, outside a geofence, etc.), microlocation(e.g., determined using in-vehicle microlocation beacons, etc.), indoorpositioning system data, local positioning system data, multilaterationdata, GSM localization data, self-reported positioning, control planelocating data, compass data, magnetometer data, route data (e.g.,origin, destination, location along a route, etc.), and/or any othersuitable location-related data. In an example, GPS data can be leveragedfor complete PVA solutions, but can additionally or alternativelyinclude any movement data, such as retrieved using GNSS data (e.g., viaGLONASS, Galileo, BeiDou, etc.). In a specific example, Block S110 caninclude determining change in relative distance between a plurality ofuser devices (e.g., residing in different vehicles, arranged in vehiclesproximal to a primary vehicle, located in vehicles within apredetermined distance of one another such as 150 ft, 1-500 ft, etc.) todetermine PVA parameters corresponding to vehicles associated with theplurality of user devices. However, collecting one or more locationdatasets can be performed in any suitable manner.

Block S110 preferably includes collecting one or more motion datasets,which can include one or more of: accelerometer data (e.g., single-axisdata, multi-axis data), gyroscope data (e.g., single-axis data,multi-axis data), velocity data (e.g., speed, instantaneous velocity,average velocity, change in velocity, velocity variability over time,maximum velocity, minimum velocity, etc.), acceleration data (e.g.,instantaneous acceleration, gravitational acceleration, averageacceleration, change in acceleration, acceleration variability overtime, maximum acceleration, minimum acceleration, etc.), displacementdata, orientation data, rotation data, turning data, and/or any othersuitable motion-related data. In an example, Block S110 can includecollecting motion datasets sampled at one or more inertial sensors(e.g., accelerometers, etc.) of the user device, where the motiondatasets can be indicative of acceleration movement features, brakingmovement features, and/or other suitable movement features for thevehicle. In another example, Block S110 can include collecting aplurality of motion datasets corresponding to a set of user devicesassociated with a set of geographically proximal vehicles; and storingthe plurality of motion datasets in association with each other (e.g.,for subsequent processing to generate comparisons between relativemotion in determining one or more traffic-related characteristics), butassociating motion datasets (and/or other datasets) can be based on anysuitable criteria. In a related example, Block S110 can includeidentifying a set of user devices corresponding to a set of secondaryvehicles driving proximal the vehicle location during a driving session,and retrieving a set of movement datasets collected by the set of userdevices contemporaneously with the driving session. However, collectingmotion datasets can be performed in any other suitable manner.

In relation to Block S110, movement datasets can be collected by aplurality of mobile devices. In variations, Block S110 can includecooperative data capture based on mobile device data (e.g., datacollected using a plurality of mobile computing devices). Thus, BlockS110 can include any element substantially as described in U.S.application Ser. No. 15/921,152, filed 14 Mar. 2018, titled “Method forMobile Device-Based Cooperative Data Capture”, which is incorporatedherein in its entirety by this reference.

In relation to Block S110, movement datasets (and/or supplementaldatasets) are preferably sampled by components arranged at a user device(e.g., mobile device, smartphone, laptop, tablet, smart watch, smartglasses, virtual reality devices, augmented reality devices, aerialdevices such as drones, medical devices, etc.), but can additionally oralternatively be sampled by components associated with (e.g., arrangedat, positioned within, mounted to, physically connected to, etc.) anysuitable device and/or vehicle (e.g., movement datasets sampled atvehicle sensors). In a specific example, Block S110 can includecollecting movement data from a population of mobile devices associatedwith a user characteristic (e.g., a shared driving behavior, etc.)and/or vehicle characteristic (e.g., a shared driving session route,etc.), where the population-level data can be used to generate trafficcharacteristic models, determine reference traffic-relatedcharacteristic (e.g., expected speed limit for a driving session routeat the current time, etc.), and/or otherwise determine and/or leveragetraffic-related characteristics. In another example, Block S110 caninclude collecting movement data for a first vehicle from devices ingeographically proximal vehicles (e.g., a second vehicle), from devicesassociated with pedestrians, and/or from other supplementary devices.Multiple sources of movement data can be used to reduce noise (e.g.,through averaging data), improve data accuracy, fill gaps of movementdata during a time period, increase confidence levels associated withdetermining traffic-related characteristics and/or initiatingtraffic-related actions, and/or perform any suitable operation inrelation to the method 100. However, collecting movement data S110 canbe performed in any suitable manner.

3.2 METHOD—COLLECTING SUPPLEMENTARY DATA

The method 100 can additionally or alternatively include Block S120,which recites: collecting supplementary dataset configured to supplementthe movement dataset. Block S120 functions to collect data that can beused in combination with, to filter, to control for errors in, and/orotherwise supplement movement data collected in Block S110, and/or othersuitable data associated with the method 100.

In relation to Block S120, supplementary data can include any one ormore of: traffic data (e.g., type of vehicular path such as a freewayroad or local road upon which the vehicle is driving; accident data;traffic level; type of traffic congestion; etc.), vehicle data (e.g.,indicative of vehicle information describing one or more characteristicsof one or more vehicles; etc.), user data (e.g., behavioral datasetssuch as datasets describing driving behavior; social media datasets;demographic datasets; device event datasets; etc.), optical data (e.g.,imagery; video; internal vehicle-facing optical data of users and/orother objects; external vehicle-facing optical data of route, landmarks,geographical markers; mobile computing device camera data; etc.), audiodata (e.g., associated with vehicle operation and/or indicative ofmovement features, such as audio corresponding to vehicle turn signaloperation, audio corresponding to acceleration and/or braking; audioassociated with user conversations; traffic-related audio; audioassociated with interactions between users and vehicles, betweenvehicles and proximal vehicles; environmental audio; etc.), biometricdata (e.g., cardiovascular parameters, such as heart rate, which can beindicative of user driving behavior in response to differenttraffic-related situation; sleep parameters correlated with trafficparameter compliance risk, with vehicular accident events; respirationdata; biological fluid data; etc.), environmental data (e.g., weatherconditions, which can be correlated with changes in driving behavior inrelation to a traffic parameter; pressure conditions; air quality;etc.), and/or any other suitable data for facilitating traffic-relatedcharacterization and/or traffic-related actions.

In a variation, Block S120 can include collecting environmental data.Environmental data can include any one or more of: weather conditions(e.g., weather forecast, temperature, humidity, precipitation, wind,etc.), road conditions (e.g., road closure, road opening, number of roadlanes, road deterioration, etc.), pressure conditions (e.g., ambientpressure, etc.), air quality (e.g., pollution levels, etc.), and/orother suitable environmental data. In an example, a weather forecastdescribing thunderstorms proximal the driver's location (e.g., derivedfrom a location dataset collected in accordance with one or morevariations of Block S110, etc.) can indicate a higher likelihood of aweather dependent traffic characteristic (e.g., a slower average ambientspeed corresponding to geographically proximal vehicles). In anotherexample, weather data can be used to augment a traffic law used todetermine a traffic compliance parameter (e.g., wherein a posted speedlimit is reduced by 5 mph during rain, wherein a driver is required toturn on the vehicle lights in the event of rain, etc.). In anotherexample, road conditions indicating a single-lane freeway, analyzedalong with a motion dataset describing a vehicular speed in excess ofthe freeway speed limit, can indicate a greater chance of occurrence ofa vehicular accident event and correspondingly higher risk parameter(e.g., determined in accordance with one or more variations of BlockS140). However, collecting environmental data can be otherwise suitablyperformed.

In a variation, Block S120 can include collecting traffic data. Trafficdata can include any one or more of: accident data (e.g., number ofaccidents within a predetermined radius of the user, accident frequency,accident rate, types of accidents, frequency of accidents, etc.),traffic level, traffic laws (e.g., speed limit, intersection laws,turning laws), traffic lights, type of vehicular path (e.g., freeway,intersection, etc.), and/or other suitable traffic data.

In an example, collecting traffic data can include querying a trafficinformation database (e.g., traffic accident tracker) with GPScoordinate inputs; and receiving a traffic report for a locationproximal the vehicle location. In another example, higher amounts oftraffic proximal the vehicle location can indicate a higher likelihoodof a multi-vehicle collision. In another example, a vehicle driverviolating traffic laws (e.g., turning left at an intersectionprohibiting left turns) can indicate a higher likelihood of a particularvehicle accident type (e.g., a T-bone collision). However, collectingtraffic data can be otherwise suitably performed.

In an example of this variation, the method 100 can include: collectingtraffic data (e.g., online sources) describing traffic parameters for alocation (e.g., county, city, state, etc.); parsing the traffic data(e.g., through applying natural language processing algorithms, whichcan be applied to any suitable portion of the method 100. etc.); andassociating the parsed traffic data with the location (e.g., geofencescorresponding to the location, etc.).

In another example, the method 100 can include: collecting optical datafor visual traffic indicators (e.g., satellite imagery capturing trafficsigns, traffic lights, traffic markings, etc.); classifying the visualtraffic indicators based on the optical data (e.g., through leveragingcomputer vision algorithms, which can be applied to any suitable portionof the method 100; etc.); and updating a traffic map (e.g., stored at atraffic-related characteristic database and usable for any suitableportion of the method 100) with the classified visual trafficindicators. In another example, the method 100 can include: collectingan image dataset at an image sensor of the mobile computing device,wherein the image sensor of the mobile computing device is arrangedwithin the vehicle to image a spatial region forward of the vehicle;extracting a visual traffic indicator from the image dataset; validatinga traffic law (e.g., determined based on one or more variations of BlockS140) based on the visual traffic indicator at the mobile computingdevice; determining the traffic compliance parameter in response tovalidating the traffic law; and updating a traffic map stored at theremote computing system based on validating the traffic law. However,processing of supplementary datasets can be performed in any suitablemanner.

In a variation, Block S120 can include collecting contextual data.Contextual data can include any one or more of: temporal data (e.g.,time of day, date, etc.), driver data, mobile electronic device usage(e.g., driver texting, usage of smartphone while driving, etc.), vehiclemodel data (e.g., model, age, accident history, mileage, repair history,etc.), light sensor data (e.g., associated with a user's mobileelectronic device, etc.), and/or any other suitable contextual data.

In an example, collecting contextual data can include collecting driverbehavior data (e.g., actively collected driver data, derived frommovement data, etc.), which can be used to adjust and/or select one ormore traffic characterization models and/or traffic-relatedcharacteristic models tailored to a given driver. Additionally oralternatively, Block S120 can include any elements described in U.S.application Ser. No. 14/206,721 filed 12 Mar. 2014 and entitled “Systemand Method for Determining a Driver in a Telematic Application,” whichis incorporated herein in its entirety by this reference.

In another example, Block S120 can include collecting temporal dataindicating the time (e.g., of day, of the year, etc.) when a drivingsession is occurring. For example, certain traffic characteristicsand/or traffic laws can be correlated with certain times of day and/ordays of the year (e.g., no left turns at a given intersection betweenthe hours of 8-10 AM and 3-6 PM, a lower speed limit in a school zone attimes when children are present, etc.). In another example, mobilecomputing device usage by the driver during the driving session (e.g.,texting while driving) can provide insight into driver behaviorsaffecting the severity of traffic law noncompliance. However, collectingtemporal data can be otherwise suitably performed.

Regarding Block S120, vehicle data can include any one or more of:proximity sensor data (e.g., radar, electromagnetic sensor data,ultrasonic sensor data, light detection and ranging, light amplificationfor detection and ranging, line laser scanner, laser detection andranging, airborne laser swath mapping, laser altimetry, sonar, dataindicating PVA of proximal vehicles, etc.), vehicle camera data (e.g.,in-car cameras, exterior cameras, back-up cameras, dashboard cameras,front-view cameras, side-view cameras, image recognition data, infraredcamera, 3D stereo camera, monocular camera, etc.), engine data,odometer, altimeter, location sensor data, motion sensor data,environmental data, light sensor data, vehicle diagnostic system data,data from application programming interfaces of traffic-relatedapplications, and/or any other suitable vehicle data. In an example,collecting vehicle operation data (and/or other suitable data) caninclude receiving vehicle operation data (e.g., collected by a vehiclediagnostic system; sampled at a proximity sensor; etc.) at a user device(e.g., with an application executing on the user device) communicablyconnected to the vehicle (e.g., wirelessly paired with the vehicle;connected to the vehicle through a wired connection between a userdevice and an OBD port of the vehicle; authorized to receive data fromthe vehicle; authenticated by the vehicle; etc.); and/or receiving thevehicle operation data at a remote server (e.g., from the user device,for subsequent movement feature extraction and analysis; etc.). Inanother example, collecting vehicle operation data (and/or othersuitable data) can include directly receiving the vehicle operation datafrom the vehicle (e.g., through a vehicle Internet connection; throughvehicle registration associated with a remote server, such as through anapplication executing on a user device; etc.). Additionally oralternatively, any characteristics described in relation to movementdatasets (e.g., in Block S110) can additionally or alternatively applyto supplementary datasets (e.g., collecting supplementary datasets atany time and/or frequency from one or more mobile devices associatedwith vehicles; etc.). However, collecting supplementary datasets S120can be performed in any suitable manner.

3.3 METHOD—PROCESSING DATASETS

Block S130 recites: extracting a set of features associated withmovement of the vehicle during the time period. The set of features canbe defined as a vehicle motion characteristic (e.g., characteristic ofvehicle motion) such as the path through space of the vehicle, thetrajectory of the vehicle, PVA data associated with the vehicle, and anyother suitable characteristics of vehicle motion. Block S130 functionsto process data (e.g., collected in Blocks S110 and/or S120; output inBlocks S140 and/or S150, etc.) into a form suited for determiningtraffic-related characteristics and/or traffic-related actions.

Processing data can include any one or more of: extracting features,performing pattern recognition on data, fusing data from multiplesources, combining values (e.g., averaging values, normalizing values,etc.), standardizing, validating, converting (e.g., digital-to-analogconversion, analog-to-digital conversion), wave modulating, filtering(e.g., Kalman filtering), noise reduction, smoothing, model fitting,transforming, windowing, clipping, mapping, applying mathematicaloperations (e.g., derivatives, moving averages, etc.), multiplexing,demultiplexing, extrapolating, interpolating, and/or any other suitableprocessing operations. In a variation, processing data can includeapplying one or more computer-implemented rules (e.g., featureengineering rules for extracting features from one or more datasets) inprocessing data for conferring improvements (e.g., in accuracy ofdetermining values of traffic-related characteristics; in storing and/orretrieving data such as traffic characteristic models, associatedinputs, and/or associated outputs; in inventive distributions offunctionality across networks including user devices, vehicles, remotecomputing systems, and/or other components; in leveragingnon-generalized systems including location sensors, inertial sensors,proximity sensors, and/or other suitable systems; etc.) in one or moreaspects of the system. Features (e.g., movement features, supplementaryfeatures) can include any one or more of: cross-user features (e.g.,features combining PVA data derived from a plurality of user devicesassociated with a single vehicle or with a plurality of vehicles; etc.),cross-vehicle features (e.g., features analyzing PVA data from a set ofproximal vehicles within a predetermined distance threshold from avehicle associated with a current driving session; etc.), textualfeatures (e.g., word frequency, sentiment, punctuation associated withtextually-described traffic laws and/or regulations; textual featuresassociated with words, numbers, and/or symbols on a traffic indicatorsuch as a sign displaying one or more words; etc.), graphical features(e.g., color, size, shape, and/or other graphical features associatedwith traffic indicators, such as color of traffic signs, traffic lights,or traffic markings on a road; etc.), audio features (e.g., MelFrequency Cepstral Coefficients extracted from audio captured by a userdevice during a driving session; etc.), and/or any other suitable typesof features.

In relation to Block S130, movement features preferably characterize PVAof the vehicle, user device, and/or other suitable entity. Additionallyor alternatively, movement features can include any one or more of: rawmovement data (e.g., raw location data, raw motion data, etc.),processed movement data, movement profiles (e.g., driving profile,braking profile, position profile, speed profile, acceleration profile,turning profile, etc.), identified driving actions (e.g., parking,acceleration, braking, short following, lane-departure, freewheeling,U-turn, left turn, right turn, over-revving, stationary vehicle, movingvehicle, etc.), user physical activity features (e.g., usercardiovascular parameters; walking pace; walking routes to and/or fromvehicles; breathing parameters; physical activity characteristicsbefore, during, and/or after driving sessions; etc.), and/or any othersuitable features. In a variation, Block S130 can include applying afeature selection rule (e.g., feature selection algorithms such asexhaustive, best first, simulated annealing, greedy forward, greedybackward, and/or other suitable feature selection algorithms) to filter,rank, and/or otherwise select features for use in generating one or moretraffic characterization models (e.g., in Block S130). Feature selectionrules can select features based on optimizing for processing speed(e.g., model training and/or execution speed; data retrieval speed;etc.), accuracy (e.g., for determining traffic-related characteristics),traffic-related actions (e.g., determining traffic-related actionstailored to a user, vehicle, traffic-related situation, etc.) and/or anyother suitable criteria operable to improve the system 200. Additionallyor alternatively, computer-implemented rules can be applied inperforming various portions of the method 100 (e.g., model generationand/or selection rules for characterizing users; user preference rulesfor promoting traffic-related actions; etc.) and/or in conferring anysuitable improvements to the system 200. However, determining featuresand/or otherwise processing datasets can be performed in any suitablemanner.

3.4 METHOD—DETERMINING A TRAFFIC-RELATED CHARACTERISTIC

Block S140 includes determining one or more traffic-relatedcharacteristics associated with the vehicle based on the set of features(and/or any other suitable datasets, such as data collected in BlocksS110 and/or S120, historical movement datasets associated with userdevices that have been arranged in vehicles traversing a proximallocation as that of the primary vehicle at which the mobile computingdevice is located, and/or outputs associated with Block S140 and/orBlock S150, etc.). Block S140 functions to evaluate, characterize,determine, store, update, and/or otherwise determine one or moretraffic-related characteristics associated with one or more vehiclesand/or users. Traffic-related characteristics preferably characterizeone or more interactions (e.g., physical interactions, relative movementfeatures, communicative interactions, etc.) between a vehicle and one ormore of: supplementary vehicles (e.g., geographically proximal vehicles;etc.), traffic parameters (e.g., traffic laws, traffic indicators,etc.), users, user devices, pedestrians, and/or any other aspectassociated with traffic. Traffic-related characteristics can include anyone or more of: traffic compliance parameters (e.g., describingcompliance with and/or deviation from traffic laws, traffic indicators,and/or other suitable traffic parameters; based on comparison ofmovement features and/or other suitable data for a driving session totraffic parameters associated with the driving session, such as trafficlaws for locations of the driving session; etc.), traffic law parameters(e.g., predicted traffic laws, such as estimated speed limit based onPVA for a geographically proximal set of vehicles; operative trafficlaws, such as derived from comparisons of predicted traffic laws toactual traffic laws; etc.), traffic indicator parameters (e.g.,identified traffic indicators such as U-turn legality based onhistorical movement features for vehicles at an intersection; operativetraffic indicators, such as derived from predicted traffic indictors toactual traffic indicators; etc.), route parameters (e.g., traffic-basedroutes from origin to destination; estimated time to destination;vehicular accident events on route; etc.), risk parameters (e.g., riskof incompliance with traffic parameters; risk relative to other usersand/or vehicles; risk of vehicular accident events; risk of user injury;insurance-related risks; energy-related risks such as fuel depletion;wherein risk can be expressed as a probability value, an expected valueover a predetermined time period or predetermined distance traveled, anactuarial table, etc.), and/or any other suitable parameters related totraffic. In variations, Block S140 can include determiningtraffic-related characteristic tailored to one or more conditions (e.g.,temporal conditions, location conditions, vehicle conditions). Inspecific examples, Block S140 can include estimating an operative speedlimit (and/or other suitable traffic-related characteristic) for alocation (e.g., a particular road on a route; etc.), time (e.g., at aparticular time of day, etc.), vehicle type (e.g., fleet vehicles, cargotrucks, motorcycles, taxis, buses, tow vehicles, etc.), and/or any othersuitable criteria. In an example, wherein the traffic law defines aprohibition against executing a first traffic maneuver at the vehiclelocation, the method 100 can include determining a second trafficmaneuver executed by the vehicle during the driving session at thevehicle location, and determining the traffic compliance parameter basedon a comparison between the first traffic maneuver and the secondtraffic maneuver (e.g., comparing accelerometer signals corresponding toa U-turn executed by the vehicle to the U-turn defined by the trafficlaw as prohibited). However, traffic-related characteristics can beotherwise suitably determined in any suitable manner.

In variations, Block S140 includes determining a risk parameter (e.g.,based on the traffic related characteristics; wherein thetraffic-related characteristic is the risk parameter; etc.). The riskparameter is preferably determined according to a risk model associatedwith the user; Block S140 can include generating the risk model and/orutilizing the risk model to determine the risk parameter. Thus, BlockS140 can include any element described in U.S. application Ser. No.16/000,675, filed 5 Jun. 2018, and titled “Method and System for RiskModeling in Autonomous Vehicles”, which is incorporated herein by thisreference in its entirety. However, Block S140 can additionally oralternatively include determining a risk parameter in any other suitablemanner.

Regarding Block S140, determining traffic-related characteristics ispreferably based on one or more movement features (e.g., derived fromdatasets sampled at a user smartphone), but can additionally oralternatively be based on any suitable datasets. In a variation,determining traffic-related characteristics can be based on PVA data(e.g., movement features extracted from location data and/or motiondata). In an example, Block S140 can include determining a trafficcompliance parameter based on comparing a current vehicle speed to anaverage vehicle speed derived from speeds for geographically proximalvehicles (e.g., driving on the highway), where incompliance with atraffic law can be predicted in response to the current vehicle speedexceeding the average vehicle speed beyond a threshold amount. Inanother example, Block S140 can include determining a traffic complianceparameter based on comparing a current vehicle speed to an actual speedlimit (e.g., derived from parsing optical data of a speed limit sign forthe road on which the vehicle is traveling, inferred from historicalaverage speeds of vehicles along the same roadway on which the vehicleis traveling, retrieved from a roadway database, etc.). In anotherexample, Block S140 can include determining a risk parameter (e.g., of atraffic violation, where the risk can be presented to the user as atraffic-related notification for guidance; etc.) based on comparing acurrent vehicle speed on a road to an operative speed limit derived froma set of historical PVA data for vehicles traveling on the road. In thisvariation, Block S140 can include determining traffic complianceparameters based on location data. For example, the method 100 caninclude: identifying a set of user devices associated with a location ofthe driving session (e.g., user devices currently being used in drivingsessions at the same location or proximal location; user devicesassociated with historical driving sessions at the same location orproximal location; user devices associated with driving sessions from ashared origin and/or to a shared destination; etc.); retrieving movementdatasets for the user devices for driving sessions associated with thelocation, and determining a traffic-related characteristic (e.g.,traffic law parameter, traffic regulation, etc.) based on the movementdatasets. In another example, the method 100 can include: receiving, ata user device associated with a driving session, signal data (e.g., abroadcasted signal; a wirelessly transmitted signal; a WiFi-basedsignal; a Bluetooth-based signal; a personal area network signal; alocal area network signal; a wide area network signal) transmitted fromone or more supplemental devices (e.g., a supplemental user deviceresiding in a supplemental vehicle), supplemental vehicles (e.g., asupplemental vehicle proximal to the vehicle of the driving session;etc.), and determining one or more traffic-related characteristics basedon the signal data (e.g., deriving a traffic compliance parameter basedon relative speed between the driving session vehicle and thesupplemental vehicle from which the signal data originates, where therelative speed can be derived from the signal data, such as signalstrength and/or angle of arrival describing location over time of thesupplemental vehicle in relation to the driving session vehicle; etc.).

In this variation, Block S140 can include determining traffic-relatedcharacteristics based on motion data. For example, Block S140 caninclude determining a traffic lane parameter (e.g., legal lane changes;number of lanes; width of lanes; traffic directionality on the lane;etc.) for a road based on inertial sensor data (e.g., sampled at usersmartphones associated with driving sessions on the road) describingvehicular movement in relation to lanes of the road (e.g., lane changesby the vehicles, etc.). In another example, Block S140 can includedetermining a traffic law parameter (e.g., legality of a vehicle turn,such as left turns, U-turns, etc.) for a location based on a set ofmotion datasets describing frequency and/or type of turns exhibited atthe location (e.g., in historical driving sessions at an intersection,etc.). In another example, Block S140 can include determining a trafficindicator parameter (e.g., average duration of different modes of atraffic light, such as average duration of a green, yellow, or red lightmode of a traffic light, etc.) based on a set of motion datasetsdescribing vehicular stoppage duration (e.g., for stationary vehicles ata red light), vehicular drive-through duration (e.g., for vehiclesdriving through a green light), and/or other suitable parametersassociated with traffic indicators. However, determining traffic-relatedcharacteristics based on PVA data and/or other movement features can beperformed in any suitable manner.

In another variation, Block S140 can include determining traffic-relatedcharacteristics based on traffic data. For example, Block S140 caninclude: determining a location of a vehicle associated with a drivingsession; in response to receiving the location at a remote server,mapping the location to a set of traffic laws (e.g., derived fromtraffic data collected from a third party source) corresponding to thelocation; and determining a traffic compliance parameter based oncomparing the traffic laws against a movement dataset associated withthe feature. In another example, Block S140 can include determining arisk parameter based on accident data for a type of vehicular path(e.g., highway, which can be correlated with increased risk of vehicularaccident event; local road; etc.) included in a driving session route.In variations, Block S140 can be based on any one or more of: vehicledata (e.g., collecting at least one of radar, sonar, and/or lidar datafrom a vehicle, where the data is indicative of relative speeds ofproximal vehicles; and determining one or more traffic-relatedcharacteristics based on the relative speeds; etc.), optical data (e.g.,identifying traffic indicator parameters and/or traffic law parametersbased on imagery and/or video capturing the traffic indicators, such asa speed limit sign; determining traffic-related characteristics based onPVA data derived from optical data; etc.), audio data (e.g., identifyinga vehicle turn at an intersection based on audio data indicating turnsignal audio; and comparing the vehicle turn to traffic laws associatedwith turn legality at the intersection; etc.), user data (e.g., derivinguser perspectives regarding traffic laws and/or traffic indicators basedon social media data; and determining traffic-related characteristicsbased on the user perspectives; etc.), biometric data (e.g., determiningcardiovascular parameters during time periods associated with differenttraffic-related situations; deriving a driving behavior based on thecardiovascular parameters; and determining traffic-relatedcharacteristics based on the driving behavior; etc.). However,determining traffic-related characteristics based on supplementary datacan be performed in any suitable manner.

Block S140 preferably includes determining traffic-relatedcharacteristics with one or more traffic characteristic models (e.g.,using movement features and/or other suitable datasets for inputs;outputting traffic-related characteristics and/or associated confidencelevels; etc.) including any one or more of: probabilistic properties,heuristic properties, deterministic properties, and/or any othersuitable properties. In examples, Block S140 and/or other portions ofthe method 100 (e.g., applying a traffic-related action model fordetermining traffic-related actions) can employ machine learningapproaches including any one or more of: supervised learning,unsupervised learning, semi-supervised learning, reinforcement learning,regression, an instance-based method, a regularization method, adecision tree learning method, a Bayesian method, a kernel method, aclustering method, an associated rule learning algorithm, a neuralnetwork model, a deep learning algorithm, a dimensionality reductionmethod, an ensemble method (e.g., determining a first speed for aproximal vehicle based on processing signal data associated with theproximal vehicle using a first traffic characteristic model during atime period; determining a second speed for the proximal vehicle basedon processing optical data captured of the proximal vehicle using asecond traffic characteristic model during the time period; anddetermining a final speed for the proximal vehicle during the timeperiod based on combining the first and second speeds such as throughaveraging, weighting, and/or other processes; etc.), and/or any suitableform of machine learning algorithm. In a specific example, the method100 can include collecting a set of movement datasets for one or moreusers during historic driving sessions, the set of movement datasetsassociated with known traffic-related characteristics (e.g., route; timefrom origin to destination; compliance with traffic laws and/or trafficindicators; date; etc.); generating (e.g., training; updating; etc.) atraffic characteristic model based on the set of movement datasets andthe known traffic-related characteristics; and determining atraffic-related characteristic for a current driving session based on amovement dataset for the driving session (e.g., determining estimatedroute time based on the historical driving sessions for the route;determining a traffic compliance parameter associated with speed limitbased on comparing a speed for the current driving session to historicspeeds for the historic driving sessions along a shared route; etc.). Inanother example, wherein the vehicle motion characteristic is a vehiclespeed, wherein the traffic law is a speed limit, Block S140 includes 4comparing the vehicle speed to the speed limit, and determining a riskparameter based on comparing the vehicle speed to the speed limit (e.g.,as described above). However, applying traffic characteristic models todetermine traffic-related characteristics can be performed in anysuitable manner.

In another variation, Block S140 can include determining atraffic-related characteristic (e.g., associated with vehicle movement)based on a set of movement datasets collected by a set of user devicescontemporaneously with the driving session (e.g., corresponding togeographically proximal vehicles), and determining a traffic complianceparameter based on a comparison between the traffic-relatedcharacteristic and the vehicle motion characteristic (e.g., a comparisonbetween an ambient speed and the vehicle speed). In a related variation,the traffic compliance parameter can be determined based on a comparisonbetween the vehicle motion characteristic and a traffic law. In stillfurther variations, the traffic compliance parameter can be determinedbased on a combination of a comparison between the traffic-relatedcharacteristic and the vehicle motion characteristic and a comparisonbetween the traffic law and the vehicle motion characteristic (e.g., toconsider the behavior of ambient traffic in evaluating user compliancewith the traffic law, as in cases where “going with the flow” of thetraffic can be acceptable even if the ambient speed is greater than thespeed limit, etc.).

In variations of Block S140, different traffic characterization models(e.g., generated with different algorithms, with different sets offeatures, with different input and/or output types, etc.) can be used(e.g., determined; selected, stored, retrieved, executed, updated, etc.)based on any one or more of the types of data associated with the method100, which can confer improvements to the system 200 by improvingtraffic characterization accuracy (e.g., by tailoring analysis to aparticular driving session, vehicle, user, and/or other entity, etc.),retrieval speed for the appropriate model from a database (e.g., byassociating tailored traffic characteristic models with particular useraccounts and/or other identifiers), training and/or execution of models(e.g., using feature-engineering computer implemented rules, etc.),and/or other suitable aspects of the system 200. However, any suitablenumber and/or type of traffic characterization models can be used in anysuitable manner. Additionally or alternatively, in relation to BlockS140, determining traffic-related characteristics can be performed usingthreshold conditions (e.g., monitoring traffic compliance parameters inresponse to PVA data exceeding a PVA threshold; determining riskparameters in response to detecting a geographically proximal lawenforcement entity; etc.), reference profiles (e.g., comparison of acurrent driving behavior profile to reference driving behavior profilesassociated with traffic law compliance or incompliance, comparison toreference traffic-related characteristics, users, user accounts,vehicles, etc.), weights, and/or any other suitable data. However,determining traffic-related characteristics S140 can be performed in anysuitable manner.

3.5 INITIATING A TRAFFIC-RELATED ACTION

The method 100 can additionally or alternatively include Block S150,which recites: initiating one or more traffic-related actions based onthe one or more traffic-related characteristics. Block S150 functions todetermine, promote, provide, and/or otherwise initiate a traffic-relatedaction for responding to determination of one or more traffic-relatedcharacteristics. Traffic-related actions can include any one or more of:traffic-related notifications, insurance processing, and/or datasetprocessing (e.g., storing traffic-related characteristics such astraffic law parameters in association with one or more vehicular pathidentifiers identifying vehicular paths; storing traffic-relatedcharacteristics such as traffic compliance parameters in associationwith one or more user accounts identifying one or more users; etc.),where such processing can confer improvements in data storage and/orretrieval, such as for generating risk profiles for users for subsequentprocessing (e.g., presentation to the user and/or associated entities;transmission to insurance entities; etc.). However, traffic-relatedactions can include any suitable type of traffic-related action.

In a variation of Block S150, initiating traffic-related notificationscan include generating, transmitting, presenting, and/or otherwisepromoting traffic-related notifications including any one or more of:educational notifications (e.g., including driving sessions summariesincluding traffic-related characteristics; including advice forimproving traffic-related characteristics such as reducing riskparameter values and/or increasing traffic compliance values, as shownin FIGS. 2-3; etc.), services notifications (e.g., for facilitatingservices such as insurance services; emergency services; routingservices, where routes can be determined for optimizing traffic-relatedcharacteristics, such as time from origin to destination; roadsideassistance services; vehicle repair services; technical services forautonomous vehicles; etc.), and/or any other suitable notifications. Forexample, as shown in FIG. 3, the method 100 can include presenting atraffic-related notification including a traffic indicator (e.g.,retrieved from a traffic-related characteristic database includingassociations between locations and traffic indicators) corresponding tolocation of a current driving session. In another example, Block S150can include promoting a roadside assistance service notification inresponse to detecting a vehicle stoppage event (e.g., a vehicle pullingover to the side of the road; etc.) in the context of proximal vehiclestraveling at greater relative speeds (e.g., vehicles passing by thevehicle pulling over; etc.). In another example, Block S150 can includepersonalizing a traffic-related notification (and/or othertraffic-related action) to a driving behavior of a user. In a specificexample, the method 100 can include determining a driving behaviorindicating a high frequency of driving through intersections when atraffic light is transitioning to a stop indicator (e.g., while atraffic light is displaying a yellow light subsequently to displaying agreen light, in advance of displaying a red light); and promoting atraffic-related notification warning indicating an upcoming trafficlight and/or a corresponding status in relation to the stop indicator(e.g., warning the user to reduce driving speed to allow for smootherbraking upon transition of a traffic signal from green to red, warningthe user that the user has increased his or her risk of running a redlight by a determined amount by his or her behavior, etc.). However,initiating traffic-related notifications can be performed any suitablemanner.

In another variation of Block S150, facilitating insurance processingcan include any one or more of: processing insurance claims (e.g.,pre-filling insurance claims, transmitting insurance claims, etc.),insurance education, transmitting traffic-related characteristics (e.g.,risk parameters; traffic compliance parameters; etc.) and/or associatedinformation (e.g., datasets collected in Block S110-S120) to aninsurance company and/or related entities or insurance entities (e.g.,insurance underwriters, primary insurers, secondary insurers, aninsurance agent, a claims adjuster, etc.), and/or any other suitableaction. In an example, Block S150 can include automatically filling(e.g., in response to a vehicular accident event; in response to amanual request by the user; etc.) and transmitting a first notice ofloss (FNOL) with information derived from traffic-relatedcharacteristics describing the vehicular accident event (e.g., speeds ofgeographically proximal vehicles; speed of a vehicle involved in thevehicular accident event; historical speeds of vehicles on the route;traffic law parameters and/or traffic indicator parameters associatedwith the location of the vehicular accident event; etc.), and/or derivedfrom any suitable datasets associated with the method 100. However,facilitating insurance processing can be performed in any suitablemanner.

In another variation of Block S150, initiating a traffic-related actioncan include controlling one or more user devices (e.g., includingsupplemental sensors from which supplemental datasets are collected) topromote the traffic-related action (e.g., through generating controlinstructions for the user devices, such as at a remote computing system,and transmitting the control instructions to the user devices; throughactivating an application executable on the user device; etc.). In anexample, Block S150 can include controlling one or more devices (e.g.,user devices, vehicles, etc.) to sample datasets, presenttraffic-related notifications, modify vehicle operation and/orparameters of a driving session (e.g., PVA parameters, route parameters,environmental parameters such as temperature and/or lighting; etc.),and/or perform any suitable process (e.g., for optimizingtraffic-related characteristics, etc.). Additionally or alternatively,controlling user devices can be performed in any suitable manner.

In another variation, Block S150 can include performing navigation basedon the traffic-related characteristic. Thus, Block S150 can include anyelement substantially as described in U.S. application Ser. No.15/243,513, filed 29 Aug. 2016, and titled “Method forAccelerometer-Assisted Navigation”, which is incorporated herein in itsentirety by this reference.

In a specific example, wherein the traffic compliance parameterindicates the vehicle motion characteristic as noncompliant with thetraffic law, Block S150 can include generating a notification indicativeof noncompliance and a corrective driver action, and providing thenotification to the user at the mobile computing device. Thenotification can a textual notification (e.g., a message rendered at adisplay of the mobile device), an audio notification (e.g., asynthesized spoken message, an audio signature indicative of aparticular traffic infraction, etc.), a visual notification (e.g., aflashing light, an image rendered on a display, etc.), and any othersuitable notification type. In another example, Block S150 includesstoring the traffic compliance parameter in association with a useraccount of the user at a remote computing system (e.g., in a database ofuser profiles and/or accounts). In another example, Block S150 includestransmitting the traffic compliance parameter to an insurance entityassociated with the user.

However, initiating traffic-related actions can additionally oralternatively be performed in any other suitable manner.

3.6 FURTHER SPECIFIC EXAMPLES

In a first specific example, as shown in FIG. 4, the method 100includes: collecting a first location dataset at a location sensor ofthe mobile computing device during a driving session defining a firsttime period of movement of the vehicle, wherein the mobile computingdevice is associated with a user; collecting a first motion dataset at amotion sensor of the mobile computing device during the driving session;extracting a vehicle motion characteristic from at least one of thelocation dataset and the motion dataset; extracting a vehicle locationfrom the location dataset; inferring a traffic law associated with thevehicle location based on a set of historical movement datasetscollected at a set of mobile computing devices associated withhistorical driving sessions intersecting the vehicle location, whereinthe historical driving sessions occur before the first time period;wherein inferring the traffic law is performed at a remote computingsystem in communication with the mobile computing device; receiving theinferred traffic law at the mobile computing device contemporaneouslywith the driving session; at the mobile computing device, determining atraffic compliance parameter associated with the vehicle motioncharacteristic based on a comparison between the vehicle motioncharacteristic and the traffic law; and in response to determining thetraffic compliance parameter, initiating a traffic-related action.

In a second specific example, as shown in FIG. 5, the method 100includes collecting a first location dataset at a location sensor of themobile computing device during a driving session defining a first timeperiod of movement of the vehicle, wherein the mobile computing deviceis associated with a user; collecting a first motion dataset at a motionsensor of the mobile computing device during the driving session;extracting a vehicle motion characteristic from at least one of thelocation dataset and the motion dataset; extracting a vehicle locationfrom the location dataset; identifying a set of user devicescorresponding to a set of geographically proximal vehicles drivingproximal the vehicle location during the driving session; determining atraffic-related characteristic associated with the movement based on aset of movement datasets collected by the set of user devicescontemporaneously with the driving session; determining a trafficcompliance parameter associated with the vehicle motion characteristicbased on a comparison between the traffic-related characteristic and thevehicle motion characteristic; and in response to determining thetraffic compliance parameter, initiating a traffic-related action.

The method 100 and/or system 200 of the embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a patient computer or mobiledevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor, though any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

The FIGURES illustrate the architecture, functionality and operation ofpossible implementations of systems, methods and computer programproducts according to preferred embodiments, example configurations, andvariations thereof. In this regard, each block in the flowchart or blockdiagrams can represent a module, segment, step, or portion of code,which includes one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block can occurout of the order noted in the FIGURES. As a person skilled in the artwill recognize from the previous detailed description and from thefigures and claims, modifications and changes can be made to theembodiments of the invention without departing from the scope of thisinvention as defined in the following claims. The embodiments includeevery combination and permutation of the various system components andthe various method processes, including any variations, examples, andspecific examples.

1. A method for traffic compliance characterization with a mobilecomputing device located within a vehicle, comprising: collecting afirst location dataset at a location sensor of the mobile computingdevice during a driving session defining a first time period of movementof the vehicle, wherein the mobile computing device is associated with auser; collecting a first motion dataset at a motion sensor of the mobilecomputing device during the driving session; extracting a vehicle motioncharacteristic from at least one of the location dataset and the motiondataset; extracting a vehicle location from the location dataset; at aremote computing system in communication with the mobile device,inferring that a visual traffic indicator is located proximal thevehicle location based on the vehicle motion characteristic; at theremote computing system, determining a traffic rule associated with thevisual traffic indicator based on the vehicle motion characteristic;mapping the visual traffic indicator and the associated traffic rule tothe vehicle location, and storing the visual traffic indicator and theassociated traffic rule in association with the vehicle location at atraffic rule map of the remote computing system.
 2. The method of claim1, wherein inferring that the visual traffic indicator is locatedproximal the vehicle location based on the vehicle motion characteristiccomprises determining that the vehicle is stopped at the vehiclelocation for a time period exceeding a threshold duration, and therebydetermining that the visual traffic indicator is a stop sign.
 3. Themethod of claim 1, wherein inferring that the visual traffic indicatoris located proximal the vehicle location based on the vehicle motioncharacteristic comprises extracting a pattern of accelerometer signalscorresponding to a deceleration period followed by an accelerationperiod, and thereby determining that the visual traffic indicator is atraffic light.
 4. The method of claim 1, wherein inferring that thevisual traffic indicator is located proximal the vehicle location basedon the vehicle motion characteristic comprises determining a currentvehicle speed based on the vehicle motion characteristic, and therebydetermining that the visual traffic indicator is a speed limitindicator.
 5. The method of claim 1, further comprising: identifying aset of user devices corresponding to a set of secondary vehicles drivingproximal the vehicle location during the driving session; determining atraffic-related characteristic associated with the movement based on aset of proximal movement datasets collected by the set of user devicescontemporaneously with the driving session; and at the remote computingsystem, determining the traffic rule associated with the visual trafficindicator based on the set of proximal movement datasets.
 6. The methodof claim 1, further comprising: collecting an image dataset at an imagesensor of the mobile computing device, wherein the image sensor of themobile computing device is arranged within the vehicle to image aspatial region forward of the vehicle; extracting visual trafficindicator data from the image dataset; validating the traffic rule basedon the visual traffic indicator data; and updating the traffic rule mapstored at the remote computing system based on validating the trafficrule.
 7. A method for traffic compliance characterization with a mobilecomputing device located within a vehicle, comprising: collecting afirst location dataset at a GPS sensor of the mobile computing deviceduring a driving session defining a first time period of movement of thevehicle, wherein the mobile computing device is associated with a user;collecting a first motion dataset at an accelerometer of the mobilecomputing device during the driving session; extracting a vehicle motioncharacteristic from at least one of the location dataset and the motiondataset; extracting a vehicle location from the location dataset;retrieving an inferred traffic rule associated with the vehicle locationfrom a traffic rule map stored at a remote computing system; determininga traffic compliance parameter associated with the vehicle motioncharacteristic based on a comparison between the inferred traffic ruleand the vehicle motion characteristic; and in response to determiningthe traffic compliance parameter, initiating a traffic-related action.8. The method of claim 7, wherein the vehicle motion characteristiccomprises a vehicle speed, wherein the inferred traffic rule comprises aspeed limit, and wherein determining the traffic compliance parametercomprises comparing the vehicle speed to the speed limit.
 9. The methodof claim 8, further comprising: collecting an optical dataset of aregion forward of the vehicle, wherein the optical dataset depicts avisual traffic indicator comprising a speed limit sign; extracting speedlimit data from the optical data of the speed limit sign; and validatingthe inferred traffic rule based on the speed limit data.
 10. The methodof claim 8, wherein the vehicle is traveling along a roadway, andwherein the inferred traffic rule is inferred, at the remote computingsystem, from historical average speeds of vehicles along the sameroadway on which the vehicle is traveling.
 11. The method of claim 7,wherein the traffic compliance parameter indicates that the vehiclemotion characteristic is noncompliant with the inferred traffic rule,and wherein the traffic-related action comprises generating anotification indicative of noncompliance and a corrective driver action,and providing the notification to the user at the mobile computingdevice.
 12. The method of claim 7, further comprising receiving weatherdata from the remote computing system, and determining the trafficcompliance parameter based on the comparison in combination with theweather data.
 13. The method of claim 7, further comprising determiningan average vehicle speed corresponding to a set of vehicles proximal thevehicle location, and determining the traffic compliance parameter basedon a combination of the comparison between the inferred traffic rule andthe vehicle motion characteristic and a comparison between the averagevehicle speed and the vehicle motion characteristic.
 14. The method ofclaim 7, wherein the inferred traffic rule defines a prohibition againstexecuting a first traffic maneuver at the vehicle location, whereindetermining the vehicle motion characteristic comprises determining asecond traffic maneuver executed by the vehicle during the drivingsession at the vehicle location, and wherein determining the trafficcompliance parameter is based on a comparison between the first trafficmaneuver and the second traffic maneuver.
 15. The method of claim 14,further comprising determining a time of day corresponding to the secondtraffic maneuver, wherein the inferred traffic rule defines aprohibition against executing the first traffic maneuver at the vehiclelocation between a first time of day and a second time of day, andwherein determining the traffic compliance parameter is based ondetermining that the time of day is between the first time of day andthe second time of day.
 16. The method of claim 7, further comprisingvalidating the inferred traffic rule retrieved from the traffic rule mapbased on the vehicle motion characteristic.
 17. The method of claim 16,further comprising updating the inferred traffic rule in response tovalidating the inferred traffic rule to generate an updated trafficrule, and storing the updated traffic rule in association with thevehicle location at the traffic rule map stored at the remote computingsystem.
 18. The method of claim 16, wherein the inferred traffic rule isassociated with a visual traffic indicator, further comprising:validating an indicator type associated with the visual trafficindicator, based on the vehicle motion characteristic, wherein thevehicle motion characteristic characterizes a vehicle interaction withthe visual traffic indicator; updating the indicator type in response tovalidating the indicator type to generate an updated indicator type; andstoring the updated indicator type in association with the vehiclelocation at the traffic rule map stored at the remote computing system.19. The method of claim 7, wherein the inferred traffic rule defines anallowable traffic maneuver at the vehicle location, wherein determiningthe vehicle motion characteristic comprises determining an actualtraffic maneuver executed by the vehicle during the driving session atthe vehicle location, and wherein determining the traffic complianceparameter is based on a comparison between the allowable trafficmaneuver and the actual traffic maneuver.
 20. The method of claim 7,further comprising: inferring a traffic rule associated with the vehiclelocation based on a set of historical motion datasets collected at a setof mobile computing devices associated with historical driving sessionsintersecting the vehicle location, wherein the historical drivingsessions occur before the first time period; wherein inferring thetraffic rule is performed at the remote computing system incommunication with the mobile computing device to generate the inferredtraffic rule.